Biology Reference
In-Depth Information
tells us how much the animal has left at the end of the day to spend on reproduction or
survival while efficiency does not. On the other hand, efficiency may be a sensible
currency when the crucial variable for the animal is not just the amount of gain, but
also the amount spent. If, for example you have to drive from A to B on a fixed amount
of fuel, efficiency might be very important indeed. It turns out that bees may be in this
position. The equivalent for a foraging bee to having a fixed amount of fuel would be if
it had a more or less fixed total lifetime capacity for expenditure of energy.
Wolf and Schmid-Hempel tested this idea by manipulating the rate of energy
expenditure of individuals, either by varying the time they were allowed to forage each
day (Schmid-Hempel & Wolf, 1988) or by fixing different sized permanent weights onto
their backs (Wolf & Schmid-Hempel, 1989). Both experiments showed that the bees
that worked hardest survived for a shorter time than controls. For example, when
workers carried a permanent weight of >20 mg, their survival was reduced from 10.8
to 7.5 days. These experiments lend some support to the hypothesis that workers, by
maximizing efficiency, might extend their lifespan and thus contribute more nectar
overall to the colony than they would by maximizing net rate.
The contrast between bees and starlings serves to underline the point that one of the
aims of economic cost-benefit analyses is to compare alternative currencies and to try
to understand why a particular currency is appropriate in each case. In each study one
of the major advantages of the quantitative analysis was that it allowed us to see when
there was a
discrepancy
between observed and predicted results. Without this potential
for discrepancy it would have been impossible, for example, to tell whether bees were
maximizing rate or efficiency, or nothing at all.
The bee example also illustrates another important point. We have been thinking of
animals as well-designed problem solvers making decisions that maximize an appropriate
currency, but of course we do not believe that bees and other animals calculate their
solutions in the same way as the behavioural ecologist. Instead the animals are
programmed to follow rules of thumb which give more or less the right answer. The bees,
for example, might use a rule that involved a threshold body weight ('if weight greater
than
x
then go home'). Schmid-Hempel (1986) investigated this by adding tiny (7 mg)
weights to the bee's back while it was foraging (Fig. 3.3b). He found that when he added
five weights at intervals during a foraging bout the bees went home with a smaller load,
as predicted if they were using a threshold weight rule. However, another experiment
showed that the rule is not this simple. Instead of adding five weights gradually, Schmid-
Hempel added five weights at the start of a foraging bout and then took them off
gradually as the bee filled its crop. These bees also went home with smaller loads than
unmanipulated bees (or than controls where the weights were placed on the bee's back
for a brief moment). The most reasonable interpretation of these results is that the bee in
some way integrates the total weight it has carried since arriving at the foraging site.
Life expectancy of
bees depends on
work load
Adding weight to
the bee's back
causes it to fly
home with a
smaller load
The economics of prey choice
The same kind of economic approach that we have used for bees and starlings can also
be used to account for the kinds of prey items that predators decide to eat.
When shore crabs are given a choice of different sized mussels they prefer the size
which gives them the highest rate of energy return (Fig. 3.4). Very large mussels
